from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-26 14:07:35.222478
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sat, 26, Dec, 2020
Time: 14:07:38
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.0928
Nobs: 152.000 HQIC: -45.1560
Log likelihood: 1636.02 FPE: 1.18544e-20
AIC: -45.8833 Det(Omega_mle): 6.68095e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.465267 0.163463 2.846 0.004
L1.Burgenland 0.143461 0.082333 1.742 0.081
L1.Kärnten -0.233286 0.066405 -3.513 0.000
L1.Niederösterreich 0.136138 0.191985 0.709 0.478
L1.Oberösterreich 0.241579 0.163623 1.476 0.140
L1.Salzburg 0.172642 0.085062 2.030 0.042
L1.Steiermark 0.072886 0.118724 0.614 0.539
L1.Tirol 0.154550 0.078532 1.968 0.049
L1.Vorarlberg 0.000483 0.076030 0.006 0.995
L1.Wien -0.136984 0.159108 -0.861 0.389
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.521218 0.212299 2.455 0.014
L1.Burgenland 0.009404 0.106931 0.088 0.930
L1.Kärnten 0.363567 0.086244 4.216 0.000
L1.Niederösterreich 0.122544 0.249343 0.491 0.623
L1.Oberösterreich -0.184860 0.212507 -0.870 0.384
L1.Salzburg 0.191973 0.110475 1.738 0.082
L1.Steiermark 0.244489 0.154194 1.586 0.113
L1.Tirol 0.145710 0.101994 1.429 0.153
L1.Vorarlberg 0.181601 0.098745 1.839 0.066
L1.Wien -0.579056 0.206644 -2.802 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.291269 0.071069 4.098 0.000
L1.Burgenland 0.107127 0.035796 2.993 0.003
L1.Kärnten -0.024991 0.028871 -0.866 0.387
L1.Niederösterreich 0.070652 0.083470 0.846 0.397
L1.Oberösterreich 0.287372 0.071138 4.040 0.000
L1.Salzburg -0.004564 0.036982 -0.123 0.902
L1.Steiermark -0.019851 0.051618 -0.385 0.701
L1.Tirol 0.090516 0.034143 2.651 0.008
L1.Vorarlberg 0.130489 0.033056 3.948 0.000
L1.Wien 0.078313 0.069176 1.132 0.258
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.183255 0.082028 2.234 0.025
L1.Burgenland -0.006547 0.041316 -0.158 0.874
L1.Kärnten 0.023476 0.033323 0.705 0.481
L1.Niederösterreich 0.026110 0.096341 0.271 0.786
L1.Oberösterreich 0.404464 0.082108 4.926 0.000
L1.Salzburg 0.096026 0.042685 2.250 0.024
L1.Steiermark 0.191401 0.059577 3.213 0.001
L1.Tirol 0.032862 0.039408 0.834 0.404
L1.Vorarlberg 0.103111 0.038153 2.703 0.007
L1.Wien -0.056373 0.079843 -0.706 0.480
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.567249 0.171292 3.312 0.001
L1.Burgenland 0.074880 0.086277 0.868 0.385
L1.Kärnten 0.009633 0.069585 0.138 0.890
L1.Niederösterreich -0.037276 0.201180 -0.185 0.853
L1.Oberösterreich 0.147829 0.171459 0.862 0.389
L1.Salzburg 0.048575 0.089136 0.545 0.586
L1.Steiermark 0.124660 0.124410 1.002 0.316
L1.Tirol 0.215483 0.082293 2.618 0.009
L1.Vorarlberg 0.015141 0.079672 0.190 0.849
L1.Wien -0.150847 0.166729 -0.905 0.366
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.160659 0.119965 1.339 0.180
L1.Burgenland -0.025400 0.060424 -0.420 0.674
L1.Kärnten -0.012134 0.048734 -0.249 0.803
L1.Niederösterreich 0.173415 0.140897 1.231 0.218
L1.Oberösterreich 0.391731 0.120082 3.262 0.001
L1.Salzburg -0.029419 0.062426 -0.471 0.637
L1.Steiermark -0.046502 0.087131 -0.534 0.594
L1.Tirol 0.191848 0.057634 3.329 0.001
L1.Vorarlberg 0.042055 0.055798 0.754 0.451
L1.Wien 0.162315 0.116769 1.390 0.165
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.213067 0.149246 1.428 0.153
L1.Burgenland 0.077246 0.075173 1.028 0.304
L1.Kärnten -0.043548 0.060629 -0.718 0.473
L1.Niederösterreich -0.033179 0.175288 -0.189 0.850
L1.Oberösterreich -0.122329 0.149392 -0.819 0.413
L1.Salzburg 0.005647 0.077664 0.073 0.942
L1.Steiermark 0.385606 0.108398 3.557 0.000
L1.Tirol 0.522290 0.071702 7.284 0.000
L1.Vorarlberg 0.219963 0.069418 3.169 0.002
L1.Wien -0.223235 0.145271 -1.537 0.124
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.122527 0.174217 0.703 0.482
L1.Burgenland 0.025607 0.087750 0.292 0.770
L1.Kärnten -0.112579 0.070773 -1.591 0.112
L1.Niederösterreich 0.231041 0.204616 1.129 0.259
L1.Oberösterreich 0.002210 0.174387 0.013 0.990
L1.Salzburg 0.220445 0.090658 2.432 0.015
L1.Steiermark 0.129897 0.126534 1.027 0.305
L1.Tirol 0.098201 0.083698 1.173 0.241
L1.Vorarlberg 0.025283 0.081032 0.312 0.755
L1.Wien 0.268403 0.169576 1.583 0.113
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.575515 0.096748 5.949 0.000
L1.Burgenland -0.016551 0.048730 -0.340 0.734
L1.Kärnten 0.003629 0.039303 0.092 0.926
L1.Niederösterreich -0.007560 0.113630 -0.067 0.947
L1.Oberösterreich 0.275061 0.096843 2.840 0.005
L1.Salzburg 0.008591 0.050345 0.171 0.865
L1.Steiermark 0.001700 0.070269 0.024 0.981
L1.Tirol 0.079012 0.046480 1.700 0.089
L1.Vorarlberg 0.178793 0.045000 3.973 0.000
L1.Wien -0.094018 0.094171 -0.998 0.318
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.138172 -0.007926 0.206961 0.241748 0.053894 0.100787 -0.093555 0.169602
Kärnten 0.138172 1.000000 -0.008378 0.185448 0.132721 -0.148307 0.172977 0.026954 0.299541
Niederösterreich -0.007926 -0.008378 1.000000 0.253601 0.074414 0.196425 0.091779 0.031179 0.348028
Oberösterreich 0.206961 0.185448 0.253601 1.000000 0.272710 0.291273 0.096038 0.070225 0.097863
Salzburg 0.241748 0.132721 0.074414 0.272710 1.000000 0.144487 0.058356 0.074945 -0.033232
Steiermark 0.053894 -0.148307 0.196425 0.291273 0.144487 1.000000 0.102703 0.083174 -0.135416
Tirol 0.100787 0.172977 0.091779 0.096038 0.058356 0.102703 1.000000 0.139988 0.129946
Vorarlberg -0.093555 0.026954 0.031179 0.070225 0.074945 0.083174 0.139988 1.000000 0.096014
Wien 0.169602 0.299541 0.348028 0.097863 -0.033232 -0.135416 0.129946 0.096014 1.000000